Zusammenfassung / Abstract

This work aims on finding new and improved approaches for broadband acoustic signal extraction. The signal extraction problem is treated as a specific source separation problem, where a single desired signal is to be separated from all remaining undesired components. For this, a generic framework is exploited, which is based on Shannon’s mutual information measure modified for nonstationary signals, and which also allows for temporal statistical dependencies of the source signals. The use of multivariate nongaussian probability density functions in the cost function also facilitates the exploitation of both the nonwhiteness and the nongaussianity of speech and audio signals. Additionally, the criterion is complemented with linear constraints for the intended separation of the desired components from the sum of all undesired components. A detailed analysis of the resulting update rule allows significant simplifications and leads to a novel Minimum Mutual Information-Generalized Sidelobe Canceler. The actual realization of this MMI-GSC depends only on some coarse prior information of the desired source position, which renders this concept very attractive for practical applications.